We now look at a different type of SVM that is designed for domain adaptation and optimizes the hyperplanes given by ws (source hyperplane) before optimizing wt (target hyperplane). The process begins by training a support vector machine on source data then once data from the target are available, train a new SVM using the hyperplane from the first SVM and the data from the target to solve for a new "domain adaptation" SVM. The primal optimization problem is given by n arg min WTE llwr||2+ C { $i Bwws yi(w7xi +b) > 1 - Ei i=1 s.t. Vi e{1,...,n} Hi e {1,...,n} fi > 0 where ws is hyperplane trained on the source data (assumed to be known), wf is hyperplane for the target, yi {+1} is the label for instance Xi, C & B are regularization parameters defined by the user and Ei is a slack variable for instance Xi. The problem becomes finding a hyperplane, WT, that minimizes the above objective function subject to the constraints. Solve/derive the dual optimization problem. We now look at a different type of SVM that is designed for domain adaptation and optimizes the hyperplanes given by ws (source hyperplane) before optimizing wt (target hyperplane). The process begins by training a support vector machine on source data then once data from the target are available, train a new SVM using the hyperplane from the first SVM and the data from the target to solve for a new "domain adaptation" SVM. The primal optimization problem is given by n arg min WTE llwr||2+ C { $i Bwws yi(w7xi +b) > 1 - Ei i=1 s.t. Vi e{1,...,n} Hi e {1,...,n} fi > 0 where ws is hyperplane trained on the source data (assumed to be known), wf is hyperplane for the target, yi {+1} is the label for instance Xi, C & B are regularization parameters defined by the user and Ei is a slack variable for instance Xi. The problem becomes finding a hyperplane, WT, that minimizes the above objective function subject to the constraints. Solve/derive the dual optimization